The present invention relates to machine learning based detection of vascular structures in medical images, and more particularly, to machine learning based detection of vessels and estimation of anisotropic vessel orientation tensors in medical images.
Automatic detection and segmentation of coronary arteries in Computed Tomography Angiography (CTA) facilitates the diagnosis, treatment, and monitoring of coronary artery diseases. Most existing coronary artery detection techniques use minimal path extraction from one or multiple seeds, operating on hand crafted cost metrics such as vesselness or medialness filters. However, these techniques do not exploit the rich image context information in CTA images and are prone to errors due to imaging artifacts, nearby touching structures such as heart chambers and coronary veins, and variation in image contrast.
An important step in coronary artery segmentation is to extract a curve along the center of the coronary artery referred to as a centerline. A centerline representation is important for the visualization of the artery through a curved planar reformatting. A centerline is also useful to support lumen segmentation methods for quantitative assessments, such as stenosis grading or CT based Fractional Flow Reserve (FFR) measurements. Coronary arteries constitute only a small portion of a large CTA volume because of their thin tubular geometry. Their centerline extraction is not an easy task due to nearby heart structures and coronary veins. Most existing coronary centerline extraction methods compute centerline paths by minimizing a vesselness or medialness cost metric, such as a Hessian based vesselness, flux based medialness, or other tubularity measure, along the paths. However, these methods are very sensitive to the underlying cost metric causing them to easily make shortcuts through nearby non-coronary structures if the cost is high along the true path, which is often the case for coronary arteries due to pathologies, large contrast variations, complex bifurcations, or imaging artifacts. In addition, because of their cost accumulative property, these methods commonly favor shorter paths causing shortcuts, particularly when tracing long curvy branches.